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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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Adversarial Attacks on Deep Neural Networks for Time Series Prediction 10

Adversarial Attacks on Deep Neural Networks for Time Series ...

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10th International Conference on Internet Computing for science and Engineering, ICICSE 2021

作者： Xu, Aidong Wang, Xuechun Zhang, Yunan Wu, Tao Xian, Xingping Guangdong Provincial Key Laboratory of Power System Network Security Electric Power Research Institute CSG China School of Computer Science and Technology Chongqing University of Posts and Telecommunications China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications China

ISBN: (纸本)9781450384957

Time series data is widespread in real-world scenarios. To recover and infer missing information in practical domains, such as stock price monitoring, electricity load forecasting, traffic flows analysis, climate trend prediction, etc., the problem of time series prediction has been widely studied as a classical research topic in data mining. Over the past decade, deep learning architectures are introduced as a vital part of the next generation of time series prediction models. However, recent studies showed that deep learning models are vulnerable to adversarial attacks. In this paper, we study the adversarial attacks on the time series prediction models prospectively. We propose an attack strategy to generate adversarial samples by adding imperceptible perturbed data to the original time series with the goal of reducing the accuracy of time series prediction models. Specifically, the perturbation-based adversarial example generation algorithm is proposed using gradient information of time series prediction model. Moreover, adversarial examples should be imperceptible to humans. To address the challenge, we craft adversarial samples based on importance measuring to perturb the original data locally. We evaluate our attacks on state-of-the-art time series prediction models using three time series datasets. Our results demonstrate that our attacks can effectively evade the time series prediction models, and the adversarial attacks mechanisms can be used as robustness metric for constructing robust time series prediction models. © 2021 ACM.

关键词： Time series

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SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation

arXiv

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arXiv 2022年

作者： Zhu, Wanqing Yin, Jia-Li Chen, Bo-Hao Liu, Ximeng Fujian Province Key Laboratory of Information Security of Network System Fuzhou350108 China College of Computer Science and Big Data Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering Yuan Ze University Taiwan

As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasing popularity. While extensive studies have been devoted to improving the model accuracy on target domain, an important issue of model robustness is neglected. To make things worse, conventional adversarial training (AT) methods for improving model robustness are inapplicable under UDA scenario since they train models on adversarial examples that are generated by supervised loss function. In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models. Based on self-training paradigm, SRoUDA starts with pre-training a source model by applying UDA baseline on source labeled data and taraget unlabeled data with a developed random masked augmentation (RMA), and then alternates between adversarial target model training on pseudo-labeled target data and fine-tuning source model by a meta step. While self-training allows the direct incorporation of AT in UDA, the meta step in SRoUDA further helps in mitigating error propagation from noisy pseudo labels. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SRoUDA where it achieves significant model robustness improvement without harming clean accuracy. Code is available at https://***/VisionFlow/SRoUDA main. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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A homogenization method for nonlinear inhomogeneous elastic materials

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Virtual Reality & Intelligent Hardware 2021年第2期3卷 156-170页

作者： Jing ZHAO Fei ZHU Liyou XU Yong TANG Sheng LI College of Information Science and Engineering Yanshan UniversityQinhuangdaoHebei 066004China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei 066004China School of Electronics Engineering and Computer Science Peking UniversityBeijing 100871China Beijing Engineering Research Center for Virtual Simulation and Visualization Beijing 100871China

Background Fast simulation techniques are strongly favored in computer graphics,especially for the nonlinear inhomogeneous elastic *** homogenization theory is a perfect match to simulate inhomogeneous deformable objects with its coarse discretization,as it reveals how to extract information at a fine scale and to perform efficient computation with much less *** existing homogenization method is not applicable for ubiquitous nonlinear materials with the limited input deformation *** In this paper,we have proposed a homogenization method for the efficient simulation of nonlinear inhomogeneous elastic *** approach allows for a faithful approximation of fine,heterogeneous nonlinear materials with very coarse *** analysis provides the basis of a linear deformation space and modal derivatives extend the space to a nonlinear regime;based on this,we exploited modal derivatives as the input characteristic deformations for *** also present a simple elastic material model that is nonlinear and anisotropic to represent the homogenized *** nonlinearity of material deformations can be represented properly with this *** material properties for the coarsened model were solved via a constrained optimization that minimizes the weighted sum of the strain energy deviations for all input deformation *** arbitrary number of bases can be used as inputs for homogenization,and greater weights are placed on the more important low-frequency *** Based on the experimental results,this study illustrates that the homogenized material properties obtained from our method approximate the original nonlinear material behavior much better than the existing homogenization method with linear displacements,and saves orders of magnitude of computational *** The proposed homogenization method for nonlinear inhomogeneous elastic materials is capable of capturing the nonlinear dynamics of the

关键词： Physical-based simulation Homogenization theory Heterogeneous material Modal basis

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Reliability Evaluation and Improvement of Islanded Microgrid Considering Operation Failures of Power Electronic Equipment

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Journal of Modern Power systems and Clean Energy 2020年第1期8卷 111-123页

作者： Wen Zhong Lingfeng Wang Zhaoxi Liu Shiying Hou State Key Laboratory of Power Transmission Equipment&System Security and New Technology College of Electrical EngineeringChongqing UniversityChongqingChina Laboratory of Trustworthy Cyber-Physical Systems and Infrastructures Department of Electrical Engineering and Computer ScienceUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

With the high integration of power electronic technologies in microgrids,the reliability assessment considering power electronic devices has become a hot ***,so far no research has considered the impact of the operation failure probability of power electronic equipment on the overall reliability of the *** paper aims to construct a holistic operation failure rate model of power electronic systems based on the overall reliability assessment of islanded microgrid with high penetration of renewable energy sources(RESs).In addition,to improve the reliability of islanded microgrid,the conventional battery energy storage system(BESS)is replaced by the hybrid energy storage system(HESS).Based on the proposed model,the operation failure models for the power electronic modules in microgrid are built and tested,and then the sensitivity analysis is performed for exploring the influence of various factors on the reliability of the microgrid.

关键词： Reliability assessment reliability improvement operation failure rate power electronics islanded microgrid

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SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in Industrial Control Through Trusted Smart Community and Lock Mechanism

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Weiguo Huang Yong Ding Jun Li Yujue Wang Hai Liang Changsong Yang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology HKCT Institute for Higher Education China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users’ accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI.

关键词： Crowdsourcing Software testing Smart cities Industrial control Smart contracts Decentralized applications Software Blockchains Software reliability security

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Improving GNSS positioning correction using Deep Reinforcement Learning with Adaptive Reward Augmentation Method 36

Improving GNSS positioning correction using Deep Reinforceme...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Tang, Jianhao Li, Zhenni Guo, Rui Zhao, Haoli Wang, Qianming Liu, Ming Xie, Shengli Polycarpou, Marios School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (纸本)9780936406350

High-precision global navigation satellite system (GNSS) positioning for automatic driving in urban environments is an unsolved problem because of the influence of multipath effects. Recently, methods based on data-driven deep reinforcement learning (DRL) have been used to learn continual positioning-correction strategies without strict assumptions about model parameters, and are adaptable to nonstationary urban environments. However, these methods face two remaining challenges: 1) real-time data for training collected in nonstationary urban environments is inadequate because of issues such as response delay and signal interruption, which causes the performance degradation in DRL, 2) the existing methods use vehicle positions as the environment observations, ignoring the complex errors caused by multipath effects in urban environments. In this paper, we propose a novel DRL-based positioning-correction method with an adaptive reward augmentation method (ARAM), and use GNSS measurements instead of vehicle positions as the environment observations, for improving the GNSS positioning accuracy in nonstationary urban environments. To specify the accurate current state of the vehicle agent, we use the GNSS measurement observations, including the line-of-sight (LOS) vector and the pseudorange residual, to model complex environmental errors, and employ a long and short term memory (LSTM) module to learn the temporal aspects of the observations, which include the interference by multipath effects on the GNSS positioning in urban environments. To address the performance degradation caused by inadequate real-time training data, we employ ARAM to adaptively modify the matching of data between the source domain and target domain of the nonstationary urban environments, to leverage sufficient data from the source domain for training, and thus to improve the performance of DRL. Hence, based on ARAM and using the GNSS measurement observations, we construct an LSTM-based proximal pol

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SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in Industrial Control Through Trusted Smart Community and Lock Mechanism 23

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

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23rd IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2024

作者： Huang, Weiguo Ding, Yong Li, Jun Wang, Yujue Liang, Hai Yang, Changsong Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin China Hkct Institute for Higher Education Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology 999077 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users' accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI. © 2024 IEEE.

关键词： Software testing

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Sequential generation of self-starting diverse operations in all-fiber laser based on thulium-doped fiber saturable absorber

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Chinese Physics B 2022年第6期31卷 87-90页

作者： Pei Zhang Kaharudin Dimyati Bilal Nizamani Mustafa M.Najm S.W.Harun School of Electrical and Information Engineering Huaihua UniversityHuaihua 418008China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua 418008China Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Advanced Technology and Multidiscipline Airlangga UniversitySurabaya 60115Indonesia Institute of Computer Science and Digital Innovation UCSI UniversityKuala LumpurMalaysia

Self-starting Q-switching,Q-switched mode-locking and mode-locking operation modes are achieved sequentially in an all-fiber erbium-doped fiber laser with thulium-doped fiber saturable absorber for the first *** central wavelengths of Q-switching,Q-switched mode-locking and mode-locking operation modes are 1569.7 nm,1570.9 nm,and 1572 nm,*** mode-locking operation of the proposed fiber laser generates stable dark soliton with a repetition rate of 0.99 MHz and signal-to-noise ratio of 65 *** results validate the capability of generating soliton pulse by doped fiber saturable ***,the proposed fiber laser is beneficial to the applications of optical communication and signal processing system.

关键词： all-fiber laser Q-switched mode-locking dark soliton fiber saturable absorber

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A New and Efficient IT Knowledge Exchange Platform 2nd

A New and Efficient IT Knowledge Exchange Platform

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Sha, Jinting Yu, Wendong Wei, Hongwei Yuan, Lin Li, Zhujun Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China Shandong Tianyi Information Technology Co. Ltd. Shandong Jinan250101 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China

ISBN: (纸本)9789819619061

As information technology continues to advance, an increasing number of technicians are joining the IT field. However, the technology in this field is changing very rapidly, requiring many technicians to constantly learn new knowledge to keep pace with developments. Therefore, in this context, the creation of a user-friendly IT technology exchange platform is highly valuable, so that technicians can easily learn and interact with each other. This platform adopts Vue 2 as the front-end framework, utilizes Spring Boot for the back-end development, Elasticsearch for full-text search capabilities, MySQL for data storage, Spring security for authentication and security, and MyBatis for data persistence. Users can register accounts, create and publish articles and resources, and also like or bookmark articles and resources. In addition, the platform also supports features such as full-text search, article categorization, resource classification and so on. Testing has demonstrated that this IT knowledge exchange platform meets the basic needs of its users. At the same time, the platform offers robust security measures to protect user privacy and ensure data security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： IT knowledge exchange platform Spring Boot Vue 2

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